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Data cleansing may also involve harmonization (or normalization) of data, which is the process of bringing together data of "varying file formats, naming conventions, and columns", [2] and transforming it into one cohesive data set; a simple example is the expansion of abbreviations ("st, rd, etc." to "street, road, etcetera").
These data types are termed soft for electronic files and hard for physical media paper copies. Data sanitization methods are also applied for the cleaning of sensitive data, such as through heuristic-based methods, machine-learning based methods, and k-source anonymity. [2]
There are several types of data cleaning, that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. [26] [27] Quantitative data methods for outlier detection, can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. [28]
To securely delete the metadata of a PDF file, it is important to linearize the PDF file afterwards, otherwise changes are reversible and the metadata can be recovered. [5] [6] Metadata removal tools are also commonly used to reduce the overall sizes of files, particularly image files posted on the Web.
Data extraction involves extracting data from homogeneous or heterogeneous sources; data transformation processes data by data cleaning and transforming it into a proper storage format/structure for the purposes of querying and analysis; finally, data loading describes the insertion of data into the final target database such as an operational ...
Unstructured data: PDF files - Anonymization of text, tables, images, scanned pages. DICOM - Anonymization metadata, pixel data, overlay data, encapsulated documents. [12] Images; Removing identifying metadata from computer files is important for anonymizing them. Metadata removal tools are useful for achieving this.